Prerequisites to run Mlflow locally on your machine
To run MLflow locally, you don't need much β it is lightweight by default. But here's the breakdown of resource requirements:
1. Python (3.7+)
Ideally Python 3.8 and higher. Create a virtual environment to keep it clean.
2. Install MLflow
pip install mlflow
Optionally, you can install these libraries for data science
pip install scikit-learn pandas numpy
3. Resource Requirements
- Disk space - 1-2 GB: More if storing many model artifacts or datasets.
- RAM - 2-4 GB: MLflow itself uses very little. Your models may need more.
- CPU - 1-2 cores: Sufficient for local use.
- GPU - not needed: Mlflow doesn't use GPU, your ML models might.
Example Local Dev Setup
A typical local dev machine to run MLflow smoothly:
- OS: Ubuntu/macOS/Windows 10+
- Python: 3.10
- RAM: 8 GB (ideal, but 4 GB works fine)
- Disk: At least 10 GB free (if you're logging large models/datasets)
Running MLflow locally
Once installed:
mlflow ui
This will start MLflow tracking server at http://localhost:5000
and store runs under the local ./mlruns
directory.
Optional Add-ons
If you want more production-like setup:
- PostgreSQL/MySQL as backend store
- MinIO / S3 for artifact storage
- Serve models using
mlflow models serve
Up next you can see MLflow workflow and how we can deploy ML model using it.